你要的答案或许都在这里：小鹏的博客目录
这是一个使用lstm预测的例子：
简单来说就是通过前几个是时刻的在线人数，预测下一个时刻的人数：（最下面有txt数据）
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 7 21:27:30 2017
@author: liupeng
"""
import numpy
import matplotlib.pyplot as plt
from pandas import read_csv
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
# matplotlib inline
import tensorflow as tf
import matplotlib.pyplot as plt
import os
import numpy as np
import csv
import string
import requests
import io
from zipfile import ZipFile
from tensorflow.contrib import learn
from tensorflow.python.framework import ops
ops.reset_default_graph()
# load the dataset
text_data = open('2017-05-26.txt')
print text_data
text_data = [x.split(' ') for x in text_data]
save_file_name = 'temp_spam_data.csv'
with open(save_file_name, 'w') as temp_output_file:
writer = csv.writer(temp_output_file)
writer.writerows(text_data)
dataframe = read_csv(save_file_name , usecols=[2], engine='python', skipfooter=3)
print dataframe
dataset = dataframe.values
# 将整型变为float
dataset = dataset.astype('float32')
plt.plot(dataset)
plt.show()
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return numpy.array(dataX), numpy.array(dataY)
# fix random seed for reproducibility
numpy.random.seed(7)
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets
train_size = int(len(dataset) * 0.70)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
# use this function to prepare the train and test datasets for modeling
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, nb_epoch=100, batch_size=1, verbose=2)
# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
print trainPredict
print testPredict
#trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
#print('Train Score: %.2f RMSE', (trainScore))
#testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
#print('Test Score: %.2f RMSE', (testScore))
# 注意数据转化
trainPredict = scaler.inverse_transform(trainPredict)
print 'trainPredict:' ,trainPredict
testPredict = scaler.inverse_transform(testPredict)
print 'testPredict', testPredict
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
看一下全部数据、训练数据和预测效果分布：（左：全部数据，右：训练和预测数据）
2017-05-25 23:51:00 218208503 -453346 2017-05-25 23:52:00 217816621 -391882 2017-05-25 23:53:00 217353051 -463570 2017-05-25 23:54:00 217017809 -335242 2017-05-25 23:55:00 216491261 -526548 2017-05-25 23:56:00 216104757 -386504 2017-05-25 23:57:00 215687986 -416771 2017-05-25 23:58:00 215316097 -371889 2017-05-25 23:59:00 214886252 -429845 2017-05-26 00:00:00 214582184 -304068 2017-05-26 00:01:00 214219827 -362357 2017-05-26 00:02:00 213859162 -360665 2017-05-26 00:03:00 213495691 -363471 2017-05-26 00:04:00 213190947 -304744 2017-05-26 00:05:00 212768694 -422253 2017-05-26 00:06:00 212481487 -287207 2017-05-26 00:07:00 212038912 -442575 2017-05-26 00:08:00 211887461 -151451 2017-05-26 00:09:00 211477626 -409835 2017-05-26 00:10:00 211202782 -274844 2017-05-26 00:41:00 199702250 -236941 2017-05-26 00:42:00 199422510 -279740 2017-05-26 00:43:00 199158710 -263800 2017-05-26 00:44:00 198883174 -275536 2017-05-26 00:45:00 198565755 -317419 2017-05-26 00:46:00 198279382 -286373 2017-05-26 00:47:00 198021028 -258354 2017-05-26 00:48:00 197758720 -262308 2017-05-26 00:49:00 197454355 -304365 2017-05-26 00:50:00 197169055 -285300 2017-05-26 00:51:00 196824191 -344864 2017-05-26 00:52:00 196585830 -238361 2017-05-26 00:53:00 196296601 -289229 2017-05-26 00:54:00 196055337 -241264 2017-05-26 00:55:00 195597581 -457756 2017-05-26 00:56:00 195401253 -196328 2017-05-26 00:57:00 195119638 -281615 2017-05-26 00:58:00 194870700 -248938 2017-05-26 00:59:00 194594592 -276108 2017-05-26 01:00:00 194318565 -276027 2017-05-26 01:01:00 194140166 -178399 2017-05-26 01:02:00 193841350 -298816 2017-05-26 01:03:00 193659637 -181713 2017-05-26 01:04:00 193391111 -268526 2017-05-26 01:05:00 193150673 -240438 2017-05-26 01:06:00 192882603 -268070 2017-05-26 01:07:00 192671204 -211399 2017-05-26 01:08:00 192435706 -235498 2017-05-26 01:09:00 192250391 -185315 2017-05-26 01:10:00 192012017 -238374 2017-05-26 01:11:00 191809664 -202353 2017-05-26 01:12:00 191560890 -248774 2017-05-26 01:13:00 191417539 -143351 2017-05-26 01:14:00 191176226 -241313 2017-05-26 01:15:00 190922209 -254017 2017-05-26 01:16:00 190783203 -139006 2017-05-26 01:17:00 190511068 -272135 2017-05-26 01:18:00 190313506 -197562 2017-05-26 01:19:00 190142329 -171177 2017-05-26 01:20:00 189849058 -293271 2017-05-26 01:20:00 189849058 -293271 2017-05-26 01:21:00 189631796 -217262 2017-05-26 01:22:00 189450977 -180819 2017-05-26 01:23:00 189450977 0 2017-05-26 01:24:00 189450977 0 2017-05-26 01:25:00 188645561 -805416 2017-05-26 01:26:00 188504537 -141024 2017-05-26 01:27:00 188249168 -255369 2017-05-26 01:28:00 188144715 -104453 2017-05-26 01:29:00 187890770 -253945 2017-05-26 01:30:00 187765838 -124932 2017-05-26 01:31:00 187512719 -253119 2017-05-26 01:32:00 187372586 -140133 2017-05-26 01:33:00 187258308 -114278 2017-05-26 01:34:00 187070711 -187597 2017-05-26 01:35:00 186947598 -123113 2017-05-26 01:36:00 186763910 -183688 2017-05-26 01:37:00 186666974 -96936 2017-05-26 01:38:00 186481813 -185161 2017-05-26 01:39:00 186382770 -99043 2017-05-26 01:41:00 186105762 -93490 2017-05-26 01:42:00 185949366 -156396 2017-05-26 01:43:00 185865019 -84347 2017-05-26 01:44:00 185865019 0 2017-05-26 01:45:00 185565614 -299405 2017-05-26 01:46:00 185419200 -146414 2017-05-26 01:47:00 185228663 -190537 2017-05-26 01:48:00 185127324 -101339 2017-05-26 01:49:00 184973254 -154070 2017-05-26 01:50:00 184879187 -94067 2017-05-26 01:51:00 184739290 -139897 2017-05-26 01:52:00 184639245 -100045 2017-05-26 01:53:00 184639245 0 2017-05-26 01:54:00 184308234 -331011 2017-05-26 01:55:00 184186250 -121984 2017-05-26 01:56:00 184045900 -140350 2017-05-26 01:57:00 183919780 -126120 2017-05-26 01:58:00 183803745 -116035 2017-05-26 01:59:00 183698147 -105598 2017-05-26 02:00:00 183555044 -143103 2017-05-26 02:00:00 183555044 -143103 2017-05-26 02:01:00 183442175 -112869 2017-05-26 02:02:00 183305486 -136689 2017-05-26 02:03:00 183195001 -110485 2017-05-26 02:04:00 183073574 -121427 2017-05-26 02:05:00 182922380 -151194 2017-05-26 02:06:00 182807065 -115315 2017-05-26 02:07:00 182713807 -93258 2017-05-26 02:08:00 182662159 -51648 2017-05-26 02:09:00 182607103 -55056 2017-05-26 02:10:00 182534281 -72822 2017-05-26 02:11:00 182439526 -94755 2017-05-26 02:12:00 182390435 -49091 2017-05-26 02:13:00 182301694 -88741 2017-05-26 02:14:00 182226365 -75329 2017-05-26 02:15:00 182110508 -115857 2017-05-26 02:16:00 182018763 -91745 2017-05-26 02:17:00 181959795 -58968 2017-05-26 02:18:00 181857044 -102751 2017-05-26 02:19:00 181815200 -41844 2017-05-26 02:21:00 181600607 -71701 2017-05-26 02:22:00 181510563 -90044 2017-05-26 02:23:00 181410749 -99814 2017-05-26 02:24:00 181211135 -199614 2017-05-26 02:25:00 181211135 0 2017-05-26 02:26:00 180874223 -336912 2017-05-26 02:27:00 180738010 -136213 2017-05-26 02:28:00 180651910 -86100 2017-05-26 02:29:00 180516821 -135089 2017-05-26 02:30:00 180444097 -72724 2017-05-26 02:31:00 180299482 -144615 2017-05-26 02:32:00 180238701 -60781 2017-05-26 02:33:00 180131439 -107262 2017-05-26 02:34:00 180048107 -83332 2017-05-26 02:35:00 179951036 -97071 2017-05-26 02:36:00 179840484 -110552 2017-05-26 02:37:00 179792785 -47699 2017-05-26 02:38:00 179736270 -56515 2017-05-26 02:39:00 179693260 -43010 2017-05-26 02:40:00 179587899 -105361 2017-05-26 02:41:00 179514296 -73603 2017-05-26 02:42:00 179441455 -72841 2017-05-26 02:43:00 179379095 -62360 2017-05-26 02:44:00 179317240 -61855 2017-05-26 02:45:00 179210449 -106791 2017-05-26 02:46:00 179156162 -54287 2017-05-26 02:47:00 179060616 -95546 2017-05-26 02:48:00 179007119 -53497 2017-05-26 02:49:00 178927519 -79600 2017-05-26 02:50:00 178846841 -80678 2017-05-26 02:50:00 178846841 -80678 2017-05-26 02:51:00 178769209 -77632 2017-05-26 02:52:00 178685373 -83836 2017-05-26 02:53:00 178629878 -55495 2017-05-26 02:54:00 178515322 -114556 2017-05-26 02:55:00 178392028 -123294 2017-05-26 02:56:00 178284387 -107641 2017-05-26 02:57:00 178221944 -62443 2017-05-26 02:58:00 178145786 -76158 2017-05-26 02:59:00 178052632 -93154 2017-05-26 03:01:00 177919509 -80130 2017-05-26 03:02:00 177871509 -48000 2017-05-26 03:03:00 177815325 -56184 2017-05-26 03:04:00 177728985 -86340 2017-05-26 03:05:00 177631513 -97472 2017-05-26 03:06:00 177514150 -117363 2017-05-26 03:07:00 177442751 -71399 2017-05-26 03:08:00 177319864 -122887 2017-05-26 03:09:00 177221814 -98050 2017-05-26 03:10:00 177124004 -97810 2017-05-26 03:11:00 177071503 -52501 2017-05-26 03:12:00 177022888 -48615 2017-05-26 03:13:00 177001750 -21138 2017-05-26 03:14:00 176971908 -29842 2017-05-26 03:15:00 176890952 -80956 2017-05-26 03:16:00 176852687 -38265 2017-05-26 03:17:00 176794636 -58051 2017-05-26 03:18:00 176774841 -19795 2017-05-26 03:19:00 176706891 -67950 2017-05-26 03:20:00 176647545 -59346 2017-05-26 03:21:00 176580657 -66888 2017-05-26 03:22:00 176526148 -54509 2017-05-26 03:23:00 176487101 -39047 2017-05-26 03:24:00 176358914 -128187 2017-05-26 03:25:00 176239507 -119407 2017-05-26 03:26:00 176120415 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2017-05-26 03:54:00 175129361 -88969 2017-05-26 03:55:00 175049993 -79368 2017-05-26 03:56:00 175011685 -38308 2017-05-26 03:57:00 174976432 -35253 2017-05-26 03:58:00 174952638 -23794 2017-05-26 03:59:00 174914420 -38218 2017-05-26 04:00:00 174870858 -43562 2017-05-26 04:00:00 174870858 -43562 2017-05-26 04:01:00 174837189 -33669 2017-05-26 04:02:00 174817383 -19806 2017-05-26 04:03:00 174793170 -24213 2017-05-26 04:04:00 174757316 -35854 2017-05-26 04:05:00 174711735 -45581 2017-05-26 04:06:00 174665500 -46235 2017-05-26 04:07:00 174668950 3450 2017-05-26 04:08:00 174754271 85321 2017-05-26 04:09:00 174773746 19475 2017-05-26 04:11:00 174733611 -26552 2017-05-26 04:12:00 174718584 -15027 2017-05-26 04:13:00 174698555 -20029 2017-05-26 04:14:00 174672204 -26351 2017-05-26 04:15:00 174632811 -39393 2017-05-26 04:16:00 174597803 -35008 2017-05-26 04:17:00 174586927 -10876 2017-05-26 04:18:00 174623020 36093 2017-05-26 04:19:00 174634022 11002 2017-05-26 04:20:00 174628733 -5289 2017-05-26 04:21:00 174620989 -7744 2017-05-26 04:22:00 174622118 1129 2017-05-26 04:23:00 174617723 -4395 2017-05-26 04:24:00 174555457 -62266 2017-05-26 04:25:00 174499735 -55722 2017-05-26 04:26:00 174456848 -42887 2017-05-26 04:27:00 174453345 -3503 2017-05-26 04:28:00 174455213 1868 2017-05-26 04:29:00 174432866 -22347 2017-05-26 04:30:00 174401772 -31094 2017-05-26 04:30:00 174401772 -31094 2017-05-26 04:31:00 174383863 -17909 2017-05-26 04:32:00 174364243 -19620 2017-05-26 04:33:00 174364243 0 2017-05-26 04:34:00 174296121 -68122 2017-05-26 04:35:00 174262566 -33555 2017-05-26 04:36:00 174231509 -31057 2017-05-26 04:37:00 174264999 33490 2017-05-26 04:38:00 174320592 55593 2017-05-26 04:39:00 174341269 20677 2017-05-26 04:41:00 174343834 2565 2017-05-26 04:42:00 174363305 19471 2017-05-26 04:43:00 174385636 22331 2017-05-26 04:44:00 174387352 1716 2017-05-26 04:45:00 174374099 -13253 2017-05-26 04:46:00 174378751 4652 2017-05-26 04:47:00 174411868 33117 2017-05-26 04:48:00 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05:43:00 180154485 190382 2017-05-26 05:44:00 180154485 0 2017-05-26 05:45:00 180448090 293605 2017-05-26 05:46:00 180575308 127218 2017-05-26 05:47:00 180789519 214211 2017-05-26 05:48:00 180983923 194404 2017-05-26 05:49:00 181227193 243270 2017-05-26 05:50:00 181350370 123177 2017-05-26 05:50:00 181350370 123177 2017-05-26 05:51:00 181546957 196587 2017-05-26 05:52:00 181692676 145719 2017-05-26 05:53:00 181936796 244120 2017-05-26 05:54:00 182048593 111797 2017-05-26 05:55:00 182145330 96737 2017-05-26 05:56:00 182313345 168015 2017-05-26 05:57:00 182478802 165457 2017-05-26 05:58:00 182826535 347733 2017-05-26 05:59:00 182970682 144147 2017-05-26 06:01:00 183366851 119148 2017-05-26 06:02:00 183610914 244063 2017-05-26 06:03:00 183802781 191867 2017-05-26 06:04:00 184039687 236906 2017-05-26 06:05:00 184266889 227202 2017-05-26 06:06:00 184440441 173552 2017-05-26 06:07:00 185068175 627734 2017-05-26 06:08:00 186012996 944821 2017-05-26 06:09:00 187041934 1028938 2017-05-26 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2017-05-26 22:59:00 237871019 -168547 2017-05-26 23:00:00 237522240 -348779 2017-05-26 23:01:00 237286865 -235375 2017-05-26 23:02:00 236913825 -373040 2017-05-26 23:03:00 236720055 -193770 2017-05-26 23:04:00 236369673 -350382 2017-05-26 23:05:00 236120729 -248944 2017-05-26 23:06:00 235711883 -408846 2017-05-26 23:07:00 235518457 -193426 2017-05-26 23:08:00 235115263 -403194 2017-05-26 23:09:00 234920241 -195022 2017-05-26 23:10:00 234419871 -500370 2017-05-26 23:10:00 234419871 -500370 2017-05-26 23:11:00 234156015 -263856 2017-05-26 23:12:00 233815164 -340851 2017-05-26 23:13:00 233553805 -261359 2017-05-26 23:14:00 233229077 -324728 2017-05-26 23:15:00 232896631 -332446 2017-05-26 23:16:00 232545009 -351622 2017-05-26 23:17:00 232203813 -341196 2017-05-26 23:18:00 231862755 -341058 2017-05-26 23:19:00 231414648 -448107 2017-05-26 23:20:00 231176238 -238410 2017-05-26 23:21:00 230594961 -581277 2017-05-26 23:22:00 230346647 -248314 2017-05-26 23:23:00 229762913 -583734 2017-05-26 23:24:00 229417307 -345606 2017-05-26 23:25:00 228872775 -544532 2017-05-26 23:26:00 228487008 -385767 2017-05-26 23:27:00 228088892 -398116 2017-05-26 23:28:00 227772296 -316596 2017-05-26 23:29:00 227381159 -391137 2017-05-26 23:31:00 226643895 -419706 2017-05-26 23:32:00 226246102 -397793 2017-05-26 23:33:00 225833039 -413063 2017-05-26 23:34:00 225466721 -366318 2017-05-26 23:35:00 225090645 -376076 2017-05-26 23:36:00 224722403 -368242 2017-05-26 23:37:00 224357403 -365000 2017-05-26 23:38:00 224068959 -288444 2017-05-26 23:39:00 223600052 -468907 2017-05-26 23:40:00 223316760 -283292 2017-05-26 23:40:00 223316760 -283292 2017-05-26 23:41:00 222854548 -462212 2017-05-26 23:42:00 222570589 -283959 2017-05-26 23:43:00 222117206 -453383 2017-05-26 23:44:00 221735500 -381706 2017-05-26 23:45:00 221289575 -445925 2017-05-26 23:46:00 220962217 -327358 2017-05-26 23:47:00 220564621 -397596 2017-05-26 23:48:00 220200498 -364123 2017-05-26 23:49:00 219800111 -400387推荐阅读：
tensorflow系列：
1. Ubuntu 16.04 安装 Tensorflow(GPU支持)
8. tf6: autoencoder—WiFi指纹的室内定位
9. tf7: RNN—古诗词
10. tf8:RNN—生成音乐
11. tf9: PixelCNN
13. tf11: retrain谷歌Inception模型
14. tf12: 判断男声女声
15. tf13: 简单聊天机器人
16. tf14: 黑白图像上色
17. tf15: 中文语音识别
19. tf17: “声音大挪移”
20. tf18: 根据姓名判断性别
21. tf19: 预测铁路客运量
26. tf24: GANs—生成明星脸
28. tf26: AI操盘手
29. tensorflow_cookbook--preface
36. 04 Support Vector Machines
37. tf API 研读1：tf.nn，tf.layers， tf.contrib概述
38. tf API 研读2：math
39. tensorflow中的上采样(unpool)和反卷积(conv2d_transpose)
40. tf API 研读3：Building Graphs
41. tf API 研读4：Inputs and Readers
44. tf.contrib.rnn.static_rnn与tf.nn.dynamic_rnn区别
45. Tensorflow使用的预训练的resnet_v2_50，resnet_v2_101，resnet_v2_152等模型预测，训练
46. tensorflow下设置使用某一块GPU、多GPU、CPU的情况
47. 工业器件检测和识别
48. 将tf训练的权重保存为CKPT,PB ,CKPT 转换成 PB格式。并将权重固化到图里面,并使用该模型进行预测
49. tensorsor快速获取所有变量，和快速计算L2范数
51. Tensorflow实战学习笔记
53. tf28: 手写汉字识别
54. tf29: 使用tensorboard可视化inception_v4
55. tf30: center loss及其mnist上的应用
58. tf33: 图片降噪：卷积自编码